How to break down a DDP

Feb 22, 2025

Identifying a Deep Domain Problem (DDP) worth solving is just the start. The real challenge is breaking it into pieces you can tackle. Here’s a systematic approach to decomposing DDPs into manageable, solvable components.

Knowledge Extraction and Mapping

1. Build a Domain Glossary

Anchor your work with a shared vocabulary:

  • Capture how experts define key terms (e.g., "risk" in finance vs. manufacturing).
  • Note stakeholder-specific variations—same word, different meanings.
  • Flag subtle distinctions (e.g., "policy" vs. "guideline").
  • Track context-shifting definitions.

This glossary aligns your team and grounds all analysis. Here is an example showcasing that words in bold are glossary words.

Example: Jane, an Influencer, posted a viral Tweet about a new Hashtag trending on X. Her Followers quickly engaged, boosting the Post Engagement with likes and retweets. The X Algorithm pushed the content to more users, increasing Jane’s Follower Count. She celebrated by sharing a Pinned Post to thank her community.

2. Chart Expert Decision-Making

Unpack how pros navigate the domain:

  • Interview varied experts on key workflows and record steps and gut calls.
  • Pinpoint inputs they weigh (e.g., data, past cases).
  • Highlight forks like "it depends"—these signal critical logic splits.
  • Compare approaches across experts to spot patterns or gaps.

These maps expose the tacit smarts driving the domain. Below is an example.

START: Loan Application
├── Credit Score ≥ 600?
│   ├── Yes
│   │   ├── DTI ≤ 40%?
│   │   │   ├── Yes
│   │   │   │   ├── Cash Flow Positive?
│   │   │   │   │   ├── Yes → APPROVE
│   │   │   │   │   └── No → REJECT
│   │   │   └── No
│   │   │       ├── Cash Flow Improving?
│   │   │       │   ├── Yes → APPROVE WITH CONDITIONS
│   │   │       │   └── No → REJECT
│   │   └── No → REJECT
│   └── No
│       ├── Strong Business Plan?
│       │   ├── Yes → APPROVE WITH STRICT TERMS
│       │   └── No → REJECT
└── END

3. Pinpoint Knowledge Sources

Locate where truth lives:

  • Formal: Regs, manuals, SOPs.
  • Informal: Expert know-how, unwritten rules.
  • Systems: Databases, legacy tools, external APIs.
  • Tribal: Shop-floor wisdom or team lore.

Prioritize what to formalize first—start with what’s reliable and accessible.

Structural Decomposition

1. Split Core from Support

Layer the problem by value:

  • Core Domain: What’s unique and high-stakes (e.g., risk scoring in compliance).
  • Supporting Domains: Essential but commodity (e.g., data entry).
  • Generic Elements: Off-the-shelf stuff (e.g., login systems).

Focus innovation on the core; leverage existing tools elsewhere.

2. Define Bounded Contexts

Big domains fragment into sub-worlds:

  • Map where concepts shift (e.g., "order" in sales vs. shipping).
  • Trace data handoffs between contexts.
  • Specify translation rules at boundaries.
  • Flag which contexts sync vs. run solo.

This keeps your solution modular, not a bloated monolith.

3. Sketch Process Trees

For key workflows:

  • Outline the "happy path" from start to finish.
  • Mark decision nodes and their triggers.
  • Chart exceptions—where it breaks and who fixes it.
  • Highlight manual choke points.

These trees reveal the complexity and prioritize pain points.

Functional Decomposition

1. List Key Capabilities

Slice the DDP into functional chunks:

  • Data gathering and cleanup.
  • Analysis and decision aids.
  • Workflow orchestration.
  • Exception tracking.
  • Reporting and auditing.

Each chunk gets a clear job, making solutions modular.

2. Set Human-Machine Roles

Divvy up the work:

  • Automate: Rule-based tasks (e.g., flagging outliers).
  • Support: Feed humans data for judgment calls.
  • Augment: Boost experts with tools (e.g., scenario simulators).
  • Oversee: Humans check complex automation.

This balance respects expertise while scaling efficiency.

3. Plan Iterative Releases

Stage your solution:

  • Start with a thin, end-to-end slice (e.g., one workflow).
  • Expand to new cases or contexts.
  • Map dependencies (e.g., data before analysis).
  • Build in feedback loops—surveys, metrics—to refine.

Testing Assumptions and Validation

1. Craft Domain Scenarios

Test your grasp with real-world cases:

  • Detail typical flows (e.g., a standard order).
  • Add edge cases that push limits.
  • Vet with experts—tweak as they correct.
  • Use these to stress-test solutions.

Scenarios double as benchmarks for success.

2. Prototype Knowledge Gaps

Spot flaws early:

  • Sketch domain models (e.g., flowcharts).
  • Draft decision trees for tricky calls.
  • Mock basic interfaces for workflows.
  • Run these by experts—fix misunderstandings fast.

These low-stakes tests save rework later.

3. Measure What Matters

Tie success to domain wins:

  • Pick KPIs experts care about (e.g., error rates, speed).
  • Baseline the current mess.
  • Set error tolerances (e.g., 5% false positives max).
  • Track tech and domain outcomes.

This keeps your focus on value, not just uptime.

Case Study: Decomposing a DDP in Supply Chain Optimization

This case study demonstrates how each checklist step applies in a real-world context, delivering measurable value while managing complexity.

Conclusion

Breaking down a DDP isn’t guesswork—it’s a deliberate mix of mapping expertise, slicing structures, and testing assumptions. Done right, you’ll turn a tangled mess into components that solve the real problem, not just its shadow. Keep domain logic and tech in sync, and you’ll build something that sticks.